AIML PROJECT IN APPLIED STATISTICS

PART ONE

Question 1

i) The Joint Probability of the people who planned to purchase and actually placed an order from the give table is given by -

P (People who actually placed an order | Total number of people) = 400/2000 = 20% or 0.2

ii) The Joint Probability of the people who planned to purchase and actually placed an order, given that people planned to purchase is given by -

P (People who actually placed an order | Total number of people who planned to purchase) = 400/500 = 80% or 0.8

Question 2

This is a Binomial Distribution Probability problem, since there are only two outcomes in this case, namely is a given bulb DEFECTIVE or NOT DEFECTIVE

A. Probability that none of the items are defective is-

B. Probability that exactly one of the items is defective is-

C. Probability that two or fewer of the items are defective is-

D. Probability that three or more of the items are defective is- 1-P(k<=2)

Question 3

This is a Poisson Distribution Probability problem, since there no upper bound and average rate of cars sold is given

A. Probability that in a given week he will sell some cars is-

B. Probability that in a given week he will sell 2 or more but less than 5 cars is-

C. Plot of the Poisson Distribution function for cumulative probability of cars sold per-week vs number of cars sold perweek-

Question 4

A. Probability that all three orders will be recognised correctly is-

B. Probability that none of the three orders will be recognised correctly is-

C. Probability that at least two of the three orders will be recognised correctly is-

Question 5

The pattern of marks follows a Normal Distribution

A. Percentage of students who score more than 80 is

B. Percentage of students who score less than 50 is

C. What should be the distinction mark if the highest 10% of students are to be awarded distinction

Question 6

One real life industry scenario where we can use the concepts of Applied statistics to get a data driven business solution is-

Data Analytics as a means to Boost Customer Acquisition and Retention

Customer is the most important asset any business depends on. Tracking online customer activity and using data to predict the next buy and chance of buy uses concepts of Applied Statistics and data Analysiss allows businesses to observe various customer related patterns and trends. Observing customer behaviour is important for boosting sales. Understanding the customer insights allow businesses to be able to deliver what the customers want.

Example - E-commerce Companies that uses Big Data Analysis to predict customer behaviour and suggest recommendations

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PART TWO

CONTEXT

The dataset contains information on all the men's top professional basketball teams of the American league system that have participated in all the past tournaments. It has data about how many baskets each team scored, conceded, how many times they came within the first 2 positions, how many tournaments they have qualified, their best position in the past, etc

OBJECTIVE

Company’s management wants to invest on proposal on managing some of the best teams in the league. The analytics department has been assigned with a task of creating a report on the performance shown by the teams. Some of the older teams are already in contract with competitors. Hence Company X wants to understand which teams they can approach which will be a deal win for them.

Read the Dataset

Checking the Dimension of data:

Clean the Dataset

Checking for Duplicates

There are NO duplicate values in the dataset

There are no missing values in the dataset

Dealing with Unexpected Missing Values in the data

Replace NaN with Median

Dataset ready for analysis

STATISTICAL ANALYSIS

Check Skewness

Check Covariance

Check Correlation

Check Mean

Check Mode

Check Median

Plotting the summery mean,mode,median using histogram

This is Right Skewed Data.

Heat Map

Heat Map showing the strength of relationship between different variables. The lighter colours show strong relationship and darker colours show weaker realtionship.

Univariate Analysis

Univariate analysis refer to the analysis of a single variable. The main purpose of univariate analysis is to summarize and find patterns in the data. The key point is that there is only one variable involved in the analysis.